1. Biomarkers as Endpoints
in Clinical Research
December 6, 2005
Victor De Gruttola
Some slides and ideas provided by Thomas Fleming

2. Biomarkers as Endpoints
Criteria for Study Endpoints
Correlates and Surrogates
Validation of Surrogates

3. Criteria for Study Endpoints in Clinical Trials
• Measurable/Interpretable
• Sensitive
• Clinically relevant
~ Prevention of Disease Related Symptoms
~ Prolongation of Survival

4. Use of Surrogate Endpoints
Treatment Effects on Surrogate Endpoints
eg: ~ Oncology: Tumor Burden Outcomes
~ HIV/AIDS: CD4, Viral Load
~ Cardiovascular Dis: B.P., Cholesterol
~ Vaccines: Antibody titers
• Establishes Biological Activity
• But Not Necessarily Clinical Efficacy

5. Biomarkers as Endpoints
Criteria for Study Endpoints
Correlates and Surrogates
Validation of Surrogates

6. Not in Causal Pathway of Disease Process
Surrogate Endpoint:
Not in Causal Pathway of Disease Process
Surrogate True Clinical
Endpoint Endpoint
Causal Pathway
Disease

7. Disease Biomarker Mother-to-Child e.g., CD4 Trans of HIV
The Surrogate Endpoint is not in the Causal Pathway of the Disease Process.
Biomarker Mother-to-Child
e.g., CD Trans of HIV
HIV Viral Load
Biomarker Ca. Symptoms e.g., CEA, PSA & Death
Tumor Burden
“Correlates”: Useful for Disease Diagnosis, or Assessing Prognosis
“Valid Surrogates”: Replacement Endpoints
Disease

8. Intervention Disease Intervention Disease
Multiple Pathways of the Disease Process
Intervention
Surrogate True Clinical
Endpoint Endpoint
Disease
Intervention
True Clinical
Endpoint
Disease
Surrogate
Endpoint

9. Intervention Disease Intervention Disease
Multiple Pathways of the Disease Process
Intervention
Transient True Clinical
Tumor Control Outcome
Disease
Intervention
True Clinical
Outcome
Disease
Tumor
Shrinkage

10. Interventions having Mechanisms of Action
Independent of the Disease Process
Intervention
Surrogate True Clinical
Endpoint Endpoint
Disease

11. Interventions having Mechanisms of Action
Independent of the Disease Process
Intervention
Arrhythmia Overall
Suppression Survival
Disease

12. Time Intervention Disease Objective Dis Related Sx
Response Rate & Death
(Negative)
Adverse treatment effects inducing significant M/M
(Positive)
Anti-tumor effects delaying long term tumor progression
Disease

13. Biomarkers as Endpoints
Criteria for Study Endpoints
Correlates and Surrogates
Validation of Surrogates

14. Validation of Surrogate Endpoints
Property of a Valid Surrogate
· Effect of the Intervention
on the Clinical Endpoint
is reliably predicted by the
Effect of the Intervention
on the Surrogate Endpoint

15. Prentice’s Sufficient Conditions
1. The surrogate endpoint
must be correlated with
the clinical outcome
2. The surrogate endpoint
must fully capture
the net effect of treatment
on the clinical outcome

16.  (t | Z,S(t) ) = 0(t) eZ +  S(t)
Z = 0 : Control ; Z = 1 : Treatment
S(t) : Surrogate Endpoint at t
(1)
 (t | Z) = 0(t) eZ
(2)
 (t | Z,S(t) ) = 0(t) eZ +  S(t)
Proportion of net treatment effect
explained by the surrogate endpoint:
DeGruttola et al, J Infectious Diseases 175: , 1997 
p = 1 - 

17. Analyses across different studies are required to explore the
validity of surrogate endpoints

18. Validation of Surrogate Endpoints
Statistical
· Meta-analyses of clinical trials data
Clinical
· Comprehensive understanding of the
~ Causal pathways of the disease process
~ Intervention’s intended and unintended
mechanisms of action

19. Assessment of Surrogates from across Multiple Studies
STEP 1
Demonstrate that a proposed marker predicts clinical response (necessary but not sufficient for a surrogate).
Determine whether the relationship between marker and clinical endpoint depends on specific treatment or is consistent across studies.

20. Estimated Hazard Ratio for Progression to AIDS/death by treatment in each trial (for each 1 log10 drop in HIV-1 RNA)

21. Estimated Hazard Ratio for Progression to AIDS/death by treatment in each trial (for each 33% increase in CD4)

22. Prediction of Endpoint Based on Marker
STEP 2
Examine the ability of the marker to predict the clinical endpoint across studies.

23. Does Marker (HIV-1 RNA) Predict Clinical Benefit Across Studies?

24. Surrogate Endpoints
Benefit of treatment partially, but not fully, mediated through viral load.
Other factors: viral fitness, resistance, toxicity, presence of HIV in compartments besides plasma. 
For trials comparing highly potent therapies to suboptimal therapies, HIV-1 RNA response to therapy may predict clinical endpoint, but HIV-1 RNA is not a surrogate endpoint in all settings.

25. Formal Prediction Approach Combining Across Studies
Goal: assessment of the reliability of predicting the effect of X on T from the effect of S on T, using information from a range of “similar” studies.
Example: Meta-analysis of the relationship between Rx effect on change CD4 count from baseline and Rx benefit (Daniels and Hughes, SIM 1997).

26. Meta-analysis of Surrogates
The impact of X on T and on S is assumed multivariate normal with mean and variance parameters that vary across studies.
By "borrowing" information regarding estimates of the effects of X on T and on the relationships between T and S given X from previous studies, one predicts effects of a new treatment from data on S.

27. Surrogate Meta-analysis
Association of log hazard ratio of AIDS or death and difference in mean change in CD4 cell count for trials that are placebo-controlled (p) and active-controlled (a). Ellipses are 95% CI’s.

28. Cross Protocol Analyses
Markers are measured with error and relationship between marker and risk of disease may be complex and vary over time.
Xu and Zeger (JRSSC, 2001) proposed a model for investigations across studies that also allowed the observed surrogate S to reflect only indirectly the true, unobservable, surrogate .
Such models accommodate situations in which S is measured with error and involve other factors besides .

29. For study 1,
For study 2, S1 S2 1 2 X1 X2 T1 T2
And in the mth study , Sm m Xm Tm

30. Hierarchy for Outcome Measures
True Clinical Efficacy Measure
Validated Surrogate Endpoint (Rare)
Non-validated Surrogate Endpoint that is
“reasonably likely to predict clinical benefit”
Correlate that is
solely a measure of Biological Activity
…Fleming (2005), Health Affairs

31. Conclusions
Validation of surrogate endpoints requires a considerable amount of information from a range of studies as well as understanding of disease mechanism.
Surrogates are most likely to be useful when applied to new drugs that are from a class that has been extensively studied and is well understood.